Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.

中文翻译：

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鉴定具有边际惩罚的基因-环境相互作用。

基因-环境（GE）相互作用分析已广泛用于复杂疾病。在边际分析中，通常的做法是对每个边际模型进行基于似然性（和其他“标准”）的估计，然后根据p值和多次比较调整选择重要的GE相互作用和主要影响。这种方法的局限性在于，识别结果通常不遵循“主要作用，相互作用”层次结构，这在最近的GE相互作用分析中已得到强调。但是，最近有一些解决方法是使用非常复杂的配方来解决这个问题。通用做法的另一个限制是，例如由于“非正态”分布，当需要进行正则化时，它可能无法很好地执行。在这篇文章中，我们提出一种边际惩罚方法，该方法采用新颖的惩罚措施来直接解决上述问题。所提出的方法具有与最近开发的联合分析方法更一致的框架和直观的表述，并且可以有效地实现。在模拟中，它优于流行的基于重要性的分析和基于惩罚的简单替代方案。在单核苷酸多态性和基因表达数据的分析中有希望的发现。它优于基于流行的基于重要性的分析和基于惩罚的简单替代方案。在单核苷酸多态性和基因表达数据的分析中有希望的发现。它优于流行的基于重要性的分析和基于惩罚的简单替代方案。在单核苷酸多态性和基因表达数据的分析中有希望的发现。